37,525 research outputs found
Detailed design specification for the ALT Shuttle Information Extraction Subsystem (SIES)
The approach and landing test (ALT) shuttle information extraction system (SIES) is described in terms of general requirements and system characteristics output products and processing options, output products and data sources, and system data flow. The ALT SIES is a data reduction system designed to satisfy certain data processing requirements for the ALT phase of the space shuttle program. The specific ALT SIES data processing requirements are stated in the data reduction complex approach and landing test data processing requirements. In general, ALT SIES must produce time correlated data products as a result of standardized data reduction or special purpose analytical processes. The main characteristics of ALT SIES are: (1) the system operates in a batch (non-interactive) mode; (2) the processing is table driven; (3) it is data base oriented; (4) it has simple operating procedures; and (5) it requires a minimum of run time information
Aspect-Controlled Neural Argument Generation
We rely on arguments in our daily lives to deliver our opinions and base them
on evidence, making them more convincing in turn. However, finding and
formulating arguments can be challenging. In this work, we train a language
model for argument generation that can be controlled on a fine-grained level to
generate sentence-level arguments for a given topic, stance, and aspect. We
define argument aspect detection as a necessary method to allow this
fine-granular control and crowdsource a dataset with 5,032 arguments annotated
with aspects. Our evaluation shows that our generation model is able to
generate high-quality, aspect-specific arguments. Moreover, these arguments can
be used to improve the performance of stance detection models via data
augmentation and to generate counter-arguments. We publish all datasets and
code to fine-tune the language model
Open-Retrieval Conversational Question Answering
Conversational search is one of the ultimate goals of information retrieval.
Recent research approaches conversational search by simplified settings of
response ranking and conversational question answering, where an answer is
either selected from a given candidate set or extracted from a given passage.
These simplifications neglect the fundamental role of retrieval in
conversational search. To address this limitation, we introduce an
open-retrieval conversational question answering (ORConvQA) setting, where we
learn to retrieve evidence from a large collection before extracting answers,
as a further step towards building functional conversational search systems. We
create a dataset, OR-QuAC, to facilitate research on ORConvQA. We build an
end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader
that are all based on Transformers. Our extensive experiments on OR-QuAC
demonstrate that a learnable retriever is crucial for ORConvQA. We further show
that our system can make a substantial improvement when we enable history
modeling in all system components. Moreover, we show that the reranker
component contributes to the model performance by providing a regularization
effect. Finally, further in-depth analyses are performed to provide new
insights into ORConvQA.Comment: Accepted to SIGIR'2
CMIR-NET : A Deep Learning Based Model For Cross-Modal Retrieval In Remote Sensing
We address the problem of cross-modal information retrieval in the domain of
remote sensing. In particular, we are interested in two application scenarios:
i) cross-modal retrieval between panchromatic (PAN) and multi-spectral imagery,
and ii) multi-label image retrieval between very high resolution (VHR) images
and speech based label annotations. Notice that these multi-modal retrieval
scenarios are more challenging than the traditional uni-modal retrieval
approaches given the inherent differences in distributions between the
modalities. However, with the growing availability of multi-source remote
sensing data and the scarcity of enough semantic annotations, the task of
multi-modal retrieval has recently become extremely important. In this regard,
we propose a novel deep neural network based architecture which is considered
to learn a discriminative shared feature space for all the input modalities,
suitable for semantically coherent information retrieval. Extensive experiments
are carried out on the benchmark large-scale PAN - multi-spectral DSRSID
dataset and the multi-label UC-Merced dataset. Together with the Merced
dataset, we generate a corpus of speech signals corresponding to the labels.
Superior performance with respect to the current state-of-the-art is observed
in all the cases
How FAIR can you get? Image Retrieval as a Use Case to calculate FAIR Metrics
A large number of services for research data management strive to adhere to
the FAIR guiding principles for scientific data management and stewardship. To
evaluate these services and to indicate possible improvements, use-case-centric
metrics are needed as an addendum to existing metric frameworks. The retrieval
of spatially and temporally annotated images can exemplify such a use case. The
prototypical implementation indicates that currently no research data
repository achieves the full score. Suggestions on how to increase the score
include automatic annotation based on the metadata inside the image file and
support for content negotiation to retrieve the images. These and other
insights can lead to an improvement of data integration workflows, resulting in
a better and more FAIR approach to manage research data.Comment: This is a preprint for a paper accepted for the 2018 IEEE conferenc
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